Data Mining And Data Visualization
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Information Visualization In Data Mining And Knowledge Discovery
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Author : Usama M. Fayyad
language : en
Publisher: Morgan Kaufmann
Release Date : 2002
Information Visualization In Data Mining And Knowledge Discovery written by Usama M. Fayyad and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Computers categories.
This text surveys research from the fields of data mining and information visualisation and presents a case for techniques by which information visualisation can be used to uncover real knowledge hidden away in large databases.
Visual Data Mining
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Author : Tom Soukup
language : en
Publisher: John Wiley & Sons
Release Date : 2002-09-18
Visual Data Mining written by Tom Soukup and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-09-18 with Computers categories.
Marketing analysts use data mining techniques to gain a reliable understanding of customer buying habits and then use that information to develop new marketing campaigns and products. Visual mining tools introduce a world of possibilities to a much broader and non-technical audience to help them solve common business problems. Explains how to select the appropriate data sets for analysis, transform the data sets into usable formats, and verify that the sets are error-free Reviews how to choose the right model for the specific type of analysis project, how to analyze the model, and present the results for decision making Shows how to solve numerous business problems by applying various tools and techniques Companion Web site offers links to data visualization and visual data mining tools, and real-world success stories using visual data mining
Data Visualization Guide
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Author : Alex Campbell
language : en
Publisher: Independently Published
Release Date : 2021-01-24
Data Visualization Guide written by Alex Campbell and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-24 with categories.
Have you ever wondered how you can work with large volumes of data sets? Do you ever think about how you can use these data sets to identify hidden patterns and make an informed decision? Do you know where you can collect this information? Have you ever questioned what you can do with incomplete or incorrect data sets? If you said yes to any of these questions, then you have come to the right place. Most businesses collect information from various sources. This information can be in different formats and needs to be collected, processed, and improved upon if you want to interpret it. You can use various data mining tools to source the information from different places. These tools can also help with the cleaning and processing techniques. You can use this information to make informed decisions and improve the efficiency and methods in your business. Every business needs to find a way to interpret and analyze large data sets. To do this, you will need to learn more about the different libraries and functions used to improve data sets. Since most data professionals use Python as the base programming language to develop models, this book uses some common libraries and functions from Python to give you a brief introduction to the language. If you are a budding analyst or want to freshen up on your concepts, this book is for you. It has all the basic information you need to help you become a data analyst or scientist. In this book, you will: Learn what data mining is, and how you can apply in different fields. Discover the different components in data mining architecture. Investigate the different tools used for data mining. Uncover what data analysis is and why it's important. Understand how to prepare for data analysis. Visualize the data. And so much more! So, what are you waiting for? Grab a copy of this book now.
Data Visualization
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Author : Alex Campbell
language : en
Publisher:
Release Date : 2020
Data Visualization written by Alex Campbell and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Data mining categories.
Making Sense Of Data Ii
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Author : Glenn J. Myatt
language : en
Publisher: John Wiley & Sons
Release Date : 2009-02-03
Making Sense Of Data Ii written by Glenn J. Myatt and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-02-03 with Mathematics categories.
A hands-on guide to making valuable decisions from data using advanced data mining methods and techniques This second installment in the Making Sense of Data series continues to explore a diverse range of commonly used approaches to making and communicating decisions from data. Delving into more technical topics, this book equips readers with advanced data mining methods that are needed to successfully translate raw data into smart decisions across various fields of research including business, engineering, finance, and the social sciences. Following a comprehensive introduction that details how to define a problem, perform an analysis, and deploy the results, Making Sense of Data II addresses the following key techniques for advanced data analysis: Data Visualization reviews principles and methods for understanding and communicating data through the use of visualization including single variables, the relationship between two or more variables, groupings in data, and dynamic approaches to interacting with data through graphical user interfaces. Clustering outlines common approaches to clustering data sets and provides detailed explanations of methods for determining the distance between observations and procedures for clustering observations. Agglomerative hierarchical clustering, partitioned-based clustering, and fuzzy clustering are also discussed. Predictive Analytics presents a discussion on how to build and assess models, along with a series of predictive analytics that can be used in a variety of situations including principal component analysis, multiple linear regression, discriminate analysis, logistic regression, and Naïve Bayes. Applications demonstrates the current uses of data mining across a wide range of industries and features case studies that illustrate the related applications in real-world scenarios. Each method is discussed within the context of a data mining process including defining the problem and deploying the results, and readers are provided with guidance on when and how each method should be used. The related Web site for the series (www.makingsenseofdata.com) provides a hands-on data analysis and data mining experience. Readers wishing to gain more practical experience will benefit from the tutorial section of the book in conjunction with the TraceisTM software, which is freely available online. With its comprehensive collection of advanced data mining methods coupled with tutorials for applications in a range of fields, Making Sense of Data II is an indispensable book for courses on data analysis and data mining at the upper-undergraduate and graduate levels. It also serves as a valuable reference for researchers and professionals who are interested in learning how to accomplish effective decision making from data and understanding if data analysis and data mining methods could help their organization.
Data Mining And Data Visualization
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Author :
language : en
Publisher: Elsevier
Release Date : 2005-05-02
Data Mining And Data Visualization written by and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-05-02 with Mathematics categories.
Data Mining and Data Visualization focuses on dealing with large-scale data, a field commonly referred to as data mining. The book is divided into three sections. The first deals with an introduction to statistical aspects of data mining and machine learning and includes applications to text analysis, computer intrusion detection, and hiding of information in digital files. The second section focuses on a variety of statistical methodologies that have proven to be effective in data mining applications. These include clustering, classification, multivariate density estimation, tree-based methods, pattern recognition, outlier detection, genetic algorithms, and dimensionality reduction. The third section focuses on data visualization and covers issues of visualization of high-dimensional data, novel graphical techniques with a focus on human factors, interactive graphics, and data visualization using virtual reality. This book represents a thorough cross section of internationally renowned thinkers who are inventing methods for dealing with a new data paradigm. - Distinguished contributors who are international experts in aspects of data mining - Includes data mining approaches to non-numerical data mining including text data, Internet traffic data, and geographic data - Highly topical discussions reflecting current thinking on contemporary technical issues, e.g. streaming data - Discusses taxonomy of dataset sizes, computational complexity, and scalability usually ignored in most discussions - Thorough discussion of data visualization issues blending statistical, human factors, and computational insights
Data Mining For Business Intelligence
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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2011-09-28
Data Mining For Business Intelligence written by Galit Shmueli and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-28 with Mathematics categories.
Praise for the First Edition " full of vivid and thought-provoking anecdotes needs to be read by anyone with a serious interest in research and marketing." —Research magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining a welcome addition to the literature." —computingreviews.com Incorporating a new focus on data visualization and time series forecasting, Data Mining for Business Intelligence, Second Edition continues to supply insightful, detailed guidance on fundamental data mining techniques. This new edition guides readers through the use of the Microsoft Office Excel add-in XLMiner for developing predictive models and techniques for describing and finding patterns in data. From clustering customers into market segments and finding the characteristics of frequent flyers to learning what items are purchased with other items, the authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, including classification, prediction, and affinity analysis as well as data reduction, exploration, and visualization. The Second Edition now features: Three new chapters on time series forecasting, introducing popular business forecasting methods including moving average, exponential smoothing methods; regression-based models; and topics such as explanatory vs. predictive modeling, two-level models, and ensembles A revised chapter on data visualization that now features interactive visualization principles and added assignments that demonstrate interactive visualization in practice Separate chapters that each treat k-nearest neighbors and Naïve Bayes methods Summaries at the start of each chapter that supply an outline of key topics The book includes access to XLMiner, allowing readers to work hands-on with the provided data. Throughout the book, applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. Each chapter concludes with exercises that allow readers to assess their comprehension of the presented material. The final chapter includes a set of cases that require use of the different data mining techniques, and a related Web site features data sets, exercise solutions, PowerPoint slides, and case solutions. Data Mining for Business Intelligence, Second Edition is an excellent book for courses on data mining, forecasting, and decision support systems at the upper-undergraduate and graduate levels. It is also a one-of-a-kind resource for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.
Data Science
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Author : Richard Hurley
language : en
Publisher:
Release Date : 2019-10-24
Data Science written by Richard Hurley and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-24 with categories.
If you want to learn about Data Science, then keep reading... This book will discuss everything that you need to know when it comes to working in the field of data science. This world has changed, and with the modern technology that we have, it is easier than ever for companies to amass a large amount of data on the industry, on their competition, on their products, and their customers. Gathering the data is the easy part, though. Being able to sort through this data and understand what it is saying is going to be a unique challenge all on its own. This is where the process and field of data science can come in. There is so much that we can explore and learn about when it comes to the world of data science, and this ultimate guide is here to help you navigate through these specialties. You will see just how important the ideas of data mining, data analytics, and even artificial intelligence are to our world as a whole today. This book will discuss the following topics: What is Data Science? What Exactly Does a Data Scientist Do? A Look at What Data Analytics Is All About What is Data Mining and How Does It Fit in with Data Science? Regression Analysis Why is Data Visualization So Important When It Comes to Understanding Your Data? How to work with Database Querying A Look at Artificial Intelligence What is Machine Learning and How Is It Different from Artificial Intelligence? What is the Future of Artificial Intelligence and Machine Learning? And much more! So if you want to learn more about data science, click "buy now"!
Data Science For Business
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Author : Herbert Jones
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-09-26
Data Science For Business written by Herbert Jones and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-26 with categories.
Do you want to learn about data science but aren't in the mood to read a boring textbook? Data science has a huge impact on how companies conduct business, and those who don't learn about this revolutionaryfield could be left behind. You see, data science will help you make better decisions, know what products and services to release, and how to provide better service to your customers. And it is all done by collecting and sorting through a large amount of information, so you have the right sources behind you when you make a major decision. In this guidebook, you will discover more about data science and how to get started in this field. This book will discuss the following topics: What is data science? How Big Data works and why it is so important How to do an explorative data analysis Working with data mining How to mine text to get the data Some amazing machine learning algorithms to help with data science How to do data modeling Data visualization How to use data science to help your business grow Tips to help you get started with data science And much, much more! So if you are ready to get started with data science, click "add to cart"!
Visual Data Mining
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Author : Simeon Simoff
language : en
Publisher: Springer
Release Date : 2008-07-23
Visual Data Mining written by Simeon Simoff and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-07-23 with Computers categories.
Visual Data Mining—Opening the Black Box Knowledge discovery holds the promise of insight into large, otherwise opaque datasets. Thenatureofwhatmakesaruleinterestingtoauserhasbeendiscussed 1 widely but most agree that it is a subjective quality based on the practical u- fulness of the information. Being subjective, the user needs to provide feedback to the system and, as is the case for all systems, the sooner the feedback is given the quicker it can in?uence the behavior of the system. There have been some impressive research activities over the past few years but the question to be asked is why is visual data mining only now being - vestigated commercially? Certainly, there have been arguments for visual data 2 mining for a number of years – Ankerst and others argued in 2002 that current (autonomous and opaque) analysis techniques are ine?cient, as they fail to - rectly embed the user in dataset exploration and that a better solution involves the user and algorithm being more tightly coupled. Grinstein stated that the “current state of the art data mining tools are automated, but the perfect data mining tool is interactive and highly participatory,” while Han has suggested that the “data selection and viewing of mining results should be fully inter- tive, the mining process should be more interactive than the current state of the 2 art and embedded applications should be fairly automated . ” A good survey on 3 techniques until 2003 was published by de Oliveira and Levkowitz .